1. Field of the Invention
This invention generally pertains to systems and methods for analyzing computer tomography data. More particularly, the present invention is directed to a method of converting computer tomography (CT) data to linear attenuation coefficient data for use in nuclear medicine, such as to compensate for attenuation in SPECT and PET imaging.
2. Description of the Related Art
Medical imaging falls into two distinct modalities or types. Transmission imaging refers to imaging such as X-ray imaging where the imaging source (e.g., X-ray) is external to the subject and is transmitted through the subject to a detector. Emission imaging refers to imaging where the imaging source (e.g., gamma-emitting radiopharmaceutical) is internal to the subject (as a result of injection or ingestion) and is emitted from the subject to a detector. Attenuation of source radiation occurs when the source radiation passes through the subject tissue, as a result of the subject absorbing or scattering some of the radiation photons. In general it is a simple matter to determine the attenuation of a discrete transmission source, since the amount of the external source being transmitted through the subject is known, and can be compared with the amount of radiation exiting the subject. However, measurement of attenuation in emission imaging is more difficult, because the accurate amount of emission source radiation being generated in the subject that results in a quantity of radiation being detected outside the subject cannot be measured directly.
Appropriate corrections for scatter and attenuation correction are prerequisites for quantitative nuclear medicine. X-ray CT image volumes can be used to derive Linear Attenuation Coefficient (LAC) maps (“mu-maps”), suitable for compensating for attenuation in single-photon-emission-computer-tomography (SPECT) and positron-emission-tomography (PET).
In general, a transmission scan is performed at an energy level other than the energy of the emission scan. Thus, the resulting attenuation map needs to be scaled to the actual emission energy of the scan, before it can be used to correct for attenuation in the emission reconstruction process. For source-based derived mu-maps, the conversion is simple because the discrete transmission and emission energies are known. For x-ray CT however, the transmission spectrum is continuous (and not discrete as it is the case for source-based methods), and, more importantly, depends upon the particular CT scanner and the attenuating body.
FIG. 1 shows that attenuation coefficients for different types of tissue depend on the energy of the photons, and can be grouped in essentially four groups, depending on their atomic number, Z: Air, soft tissue, bone, and iron, with iron representing a class of “Very High-Z” implants, such as surgical screws, hip-replacements, or other possible very high-Z materials in the body.
X-ray CT images are calibrated so that each voxel is measured in units of Hounsfield, usually defined as:HUMaterial=(μTMaterial−μTWater)/(μTWater−μTAir)*1000  (1.),In this definition μTMaterial is the linear attenuation coefficient of a given material at an “effective” transmission energy T, and μTwater is the linear attenuation coefficient of water at the same “effective” transmission energy T. The linear attenuation coefficients are “narrow beam” values, which are derived from primary photon counts only, and thus do not include any scattered photons (see also FIG. 16).
Because a CT scanner emits a continuous spectrum of x-rays, an “effective” transmission energy is not easily obtained. The HU values are normalized for all scanners and protocols if the CT scanner for clinical practice has been properly set-up, so that water always corresponds to HU=0 and air corresponds to HU=−1000 (it is noted that some definitions multiply by a factor of 210=1024; such cases are included within the scope of the invention). All clinical CT scanners have to be calibrated using a vendor specific protocol to conform to this definition. However, there is no definition for densities greater than water. For instance, the same bone tissue may have different HU values when acquired with different CT scanners. The HU value of a bone specimen may even change depending on the surrounding amount of soft tissue and reconstruction parameters on the same CT scanner. Converting bone tissue accurately is important because it may contribute largely to attenuation of emission energy.
Various approaches are known for converting CT values to linear attenuation coefficients, depending on the degree and type of approximation treating the continuous CT spectrum. These methods can be grouped in two major classes: Uniform scaling, and Non-uniform scaling.
In Uniform scaling, all pixels in the transmission slices are multiplied by the same factor K, where K is usually computed from the linear attenuation coefficient of water at the effective transmission-energy T μTWater and emission-energy E μEWater:K=μEWater/μTWater  (2).
This approach is accurate for soft tissues (low Z), since their attenuation properties are similar to water. In water, or other low-Z materials, Compton scattering is the dominant effect for clinically used emission energies, but for high-Z materials such as bone, the photoelectric absorption becomes the dominant effect. As a result, a scaling factor derived using water deviates considerably from a bone-derived scaling value. Thus, equation (2) provides inaccurate linear attenuation coefficients for materials more dense than water and soft tissue. The equation also assumes that an “effective” transmission-energy is known.
For a CT scan, the transmission energy can only be an “effective” transmission-energy, which essentially replaces the continuous transmission spectrum through the body with a mono-energetic “effective” transmission-energy. However, such an effective transmission-energy depends on the type of CT scanner, patient, and also on the CT reconstruction parameters. Examples of such methods are described in Kalki K., Brown, J. K.,et al., “Myocardial Perfusion Imaging with a Correlated X-ray CT and SPECT System: An Animal Study”, IEEE TNS, 43(3), 1996, pp. 2000-2007, 1996, which is incorporated herein by reference.
Non-Uniform Scaling Tissue method allows regions of the CT image volume to be defined as part of different classes of tissues (“segmentation”). Either each class of tissue is scaled from some “effective” transmission energy to the emission energy, or pixel values for each tissue type are replaced with the appropriate attenuation coefficients at the emission energy. Typical choices for tissue types include soft tissue, bone, lung and air. However, there are numerous problems with this approach. For example, this method does not take into account existing variations in tissues densities for the same tissue classes, it is limited to the errors of the segmentation routine, it may require an educated user to segment the data accurately, and it is not user-friendly.
Pixel-by-pixel conversion is an extension of the tissue typing approach. In this method, each pixel is scaled from CT units to linear attenuation coefficients. In principle, this method requires knowledge of the type of tissue for each pixel. This is often difficult because pixels may contain more than one tissue type (“partial volume”), or an educated user may be needed to identify the tissue type of each pixel (see, D. R. White, M. Fitzgerald, “Calculated attenuation and Energy Absorption Coefficients for ICRP Reference Man (1975) Organs and Tissues”, Health Physics, 33, pp. 73-81, 1977, which is incorporated herein by reference). Therefore, this method suffers from similar problems.
Thus, there is a need for new and improved systems and methods for creating linear attenuation coefficient maps from CT image data that improve upon and solve the foregoing problems with the prior art.